DETAILED ACTION
Notice to Applicant
The following is a FINAL Office action upon examination of application number 17/836,498, filed on 06/09/2022. Claims 1-20 are pending in the application and have been examined on the merits discussed below.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Application 17/836,498 filed 06/09/2022 claims Priority from Provisional Application 63/208,706, filed 06/09/2021.
Response to Amendment
In the response filed March 16, 2026, Applicant amended claims 1-4, 6, 11-14, and 16, and did not cancel any claims. No new claims were presented for examination.
Applicant's amendments to claim 11 are hereby acknowledged. The amendments are sufficient to overcome the previously issued claim rejection under 35 U.S.C. 112(b); accordingly, this rejection has been removed. However, a new §112(b) rejection is presented in light of the claim amendments.
Applicant's amendments to claims 1-4, 6, 11-14, and 16 are hereby acknowledged. The amendments are not sufficient to overcome the previously issued claim rejection under 35 U.S.C. 101; accordingly, this rejection has been maintained.
Response to Arguments
Applicant's arguments filed March 16, 2026, have been fully considered.
Applicant submits “These claim features are related to using supervised machine learning to automatically assign a break-down session to users based on historical data, separately machine training multiple different personalized prediction models based on multiple different personalized historic data, respectively, automatically predicting multiple scheduling parameters of the assigned session based on personalized machine-learned prediction models, accessing calendars via an application running on a computing device, automatically adding the assigned session to the users' calendars based on scheduling constraints determined based on the multiple scheduling parameters, and dynamically synchronizing a project view with a calendar view in the application to maintain consistency across the project view and the calendar view. These claim features provide training and serving pipelines that improve computerized calendar scheduling. These claim features are not related to fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Thus, claim 1 including these claim features extends far beyond methods of organizing human activity.” [Applicant’s Remarks, 03/16/2026, pages 17-18]
In response, the Examiner maintains that the claims plainly set forth or describe steps encompassing managing personal behavior or relationships or interactions, which fall under “certain methods of organizing human activity” abstract idea grouping set forth in MPEP 2106. For example, receiving from a first user of the plurality of users, information about break-down sessions for different levels of sharing, wherein a second user of the plurality of users expresses interest in the sharing and a third user does not express interest in the sharing; sending automatically a recommendation of the break-down sessions to the second user, and a notification of the break-down sessions to the third user; in response to acceptance of at least one of the break-down sessions by the second user, assigning automatically the at least one of the break-down sessions to the second user based on knowledge obtained from historic data on previous task completion experience of the second user and previous collaboration between the first user and the second user, wherein the assigned session requires presence of the first and second users; and updating a first calendar associated with the first user and a second calendar associated with the second user by: receiving information related to the assigned session; predicting automatically multiple scheduling parameters associated with the assigned session, the multiple scheduling parameters including a priority of the assigned session, a duration of the assigned session, and a start time for the assigned session; accessing each of the first and second calendars in a calendar view; determining, based on the multiple scheduling parameters and existing tasks in the first and second calendars, one or more scheduling constraints for the assigned session; automatically adding the assigned session to the first and second calendars in a manner that satisfies the one or more scheduling constraints with respect to existing tasks in the first and second calendars based on the multiple features predicted for the assigned session to generate updated first and second calendars with the assigned session scheduled therein; and dynamically synchronizing a project view with each of the updated first and second calendars to maintain consistency across the project view and the calendar view, as recited in exemplary claim 1, are reasonably understood as managing personal behavior or relationships or interactions (e.g., following rules or instructions), particularly when read in light of the Specification.
Applicant’s Specification supports the interpretation of the above-noted steps as implemented in the context of managing personal behavior or relationships or interactions. The scheduling and task management focus of the disclosed/claimed invention is evident throughout the Specification. For example, paragraph [0034] of the Specification describes that “The present teaching relates to method, system, and implementation of intelligent and personalized task management, based on learning, to help a user to manage team or personal related tasks/projects. The present teaching as disclosed herein manages tasks/projects automatically via knowledge learned from past data to assist users to effectively manage tasks with minimum required user manual interactions with a task list (unless the user wants to) to enhance efficiency and user experience” which lends further support for the finding that the claim limitations are implemented in the context of managing personal behavior or relationships or interactions. Accordingly, Applicant’s claimed invention, when read in light of the specification, clearly supports the finding that the claims fall within the realm of abstract ideas described in the “Certain Methods of Organizing Human Activities” abstract idea grouping of the MPEP 2106. It is maintained that the claims are still focused on managing interactions between multiple used, including receiving information about session preference, recommending sessions, assigning tasks based on prior collaboration, and updating calendars. These steps are quintessentially methods of organizing human activity, as they automate coordination, scheduling, and participation in collaborative tasks. For the reasons above, this argument is found unpersuasive.
Last, in response to Applicant’s argument that the claim improves calendar scheduling through machine learning and personalized prediction models, and that it does not relate to fundamental economic practices or organizing human activity, it is noted that the claimed features, however, are still directed to the abstract ideas of automating human scheduling and task assignments. The recitation of standard supervised machine learning, generic computer hardware, and conventional calendar aces and synchronization does not add an inventive step sufficient to render the claim eligible. The claim does not improve the operation of the computer itself, but instead automates a human organizational task, and therefore remains ineligible under 35 U.S.C. 101.
Applicant submits “Further, these claim features are not directed to mathematical concepts. Also, these claim features cannot be performed in human mind and cannot be done mentally or with pen and paper. Independent claim 11 recites similar recitations.” [Applicant’s Remarks, 03/16/2026, page 18]
In response, it is noted that the Office action did not assert the claim features as falling within the “Mathematical Concepts” and “Mental Processes” abstract idea groupings. Accordingly, this argument is deemed moot.
Applicant submits “Even assuming, for the sake of argument, that claim 1 (and independent claim 11) does recite an abstract idea (which the Applicant disagrees), Applicant respectfully submits that claim 1 (and independent claim 11) is patent eligible under Prong Two of the Step 2A Analysis under the Guidance and the MPEP. The recited features are clearly tied to a practical application, i.e., using supervised machine learning to automatically assign a break-down session to users based on historical data, separately machine training multiple different personalized prediction models based on different personalized historic data, automatically predicting multiple scheduling parameters of the assigned session based on the multiple different personalized machine-learned prediction models, accessing calendars via an application running on a computing device, automatically adding the assigned session to the users' calendars based on scheduling constraints determined based on the multiple scheduling parameters, and dynamically synchronizing a project view with a calendar view in the application to maintain consistency across the project view and the calendar view.” [Applicant’s Remarks, 03/16/2026, page 20]
The Examiner respectfully disagrees. Under Step 2A Prong Two of the eligibility inquiry, any additional elements are evaluated individually and in combination to determine whether they integrate the judicial exception into a practical application, with consideration of the following exemplary considerations that may be indicative of a practical application: an additional element that reflects an improvement to the functioning of a computer or to any other technology or technical field, applying the exception with a particular machine, applying the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, effecting a transformation of a particular article to a different state or thing, and applying or using the judicial exception some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
In this instance, the additional elements recited in exemplary claim 1 include: at least one machine including at least one processor, memory, communication platform, a network for online marketplace knowledge sharing, an online sharing platform connecting to a plurality of users, supervised machine learning, separately training, via supervised machine learning, a plurality of personalized prediction models, the plurality of separately trained personalized prediction models, an application running on a computing device associated with each of the first and second users, the corresponding application, and the application running on each of the computing devices associated with the first and second users. These elements have been considered individually and in combination, however these computing elements amount to using a generic computer programmed with computer-executable instructions/software to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment, which is not sufficient to amount to a practical application, as noted in MPEP 2106. See also MPEP 2106.05(f) and 2106.05(h). Furthermore, these additional elements fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Instead, the task generator amounts to using generic computing devices as tools to implement the abstract idea, which does not amount to a technological improvement or otherwise indicate a practical application. See MPEP 2106.05(f).
The Examiner emphasizes that nowhere in Applicant’s Specification is there any discussion or suggestion that the problem or solution is a technical one, nor is there even a hint of any contemplated improvement to technology. It is not clear how the claimed limitations provide an actual improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment evident in the claims. The Applicant’s claims do not adequately explain how the additional elements of the claim integrate to add any meaningful limits on the abstract idea. At the most, the claimed invention seems to provide improvement beneficial to the end users. The focus of the claims of the instant application is not on such an improvement in computers as tools, but on certain independently abstract ideas that use computers as tools. Even reviewing the Applicant’s Specification (which describes the hardware and software), it is not made clear how the hardware and software result in an improvement to the technology or hardware itself, etc. The claimed invention does not provide an improvement to another technology/technical field or the functioning of the computer itself. Applicant's invention is directed towards providing business solutions to business problems rather than providing technical solutions to technical problems; thus, the claimed invention does not provide an improvement to another technology/technical field or the functioning of the computer itself. The Examiner further points out there is no actual improvement to another technology or technical field, no improvement to the functioning of the computer itself, and no meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment evident in the claims.
Moreover, it is noted that Applicant’s claims are devoid of any discernible change, transformation, or improvement to a computer (software or hardware) or any existing technology. Applicant has not shown that any specific technological improvement is achieved within the scope of the claims. It bears emphasis that no machine, processor, memory, communication platform, online sharing platform, computing device or technological elements are modified or improved upon in any discernible manner. Instead, the result produced by the claims is simply information including the assigned session, which is not a technical result or improvement thereof. For the reasons above, this argument is found unpersuasive.
Lastly, in response to Applicant’s argument that “Even assuming, for the sake of argument, that claim 1 (and independent claim 11) does recite an abstract idea (which the Applicant disagrees), Applicant respectfully submits that claim 1 (and independent claim 11) is patent eligible under Prong Two of the Step 2A Analysis under the Guidance and the MPEP. The recited features are clearly tied to a practical application, i.e., using supervised machine learning to automatically assign a break-down session to users based on historical data, separately machine training multiple different personalized prediction models based on different personalized historic data, automatically predicting multiple scheduling parameters of the assigned session based on the multiple different personalized machine-learned prediction models, accessing calendars via an application running on a computing device, automatically adding the assigned session to the users' calendars based on scheduling constraints determined based on the multiple scheduling parameters, and dynamically synchronizing a project view with a calendar view in the application to maintain consistency across the project view and the calendar view,” the Examiner respectfully disagrees that the recited features demonstrate a practical application. While the claims mentions supervised machine learning, calendar access, and automated scheduling, these elements merely implement the abstract idea of organizing and managing human interactions using generic components. The claim does not recite any specific improvement to the operation of the computer, machine learning algorithm, or calendar synchronization, rather it uses conventional technology to automate tasks. Thus, any improvement achieved by automating the claim steps (i.e., using generic computing devices/software) is not a technical improvement, but instead would come from the capabilities of a general-purpose computer rather than the sequence of steps/activities recited in the method itself, which does not materially alter the patent eligibility of the claim. See Bancorp Servs., L.L.C. v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012) (“[T]he fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter.”) (cited in the Federal Circuit's FairWarning decision). Accordingly, this argument is found unpersuasive.
Applicant submits “the current claims are necessarily rooted in computer technology of computerized calendar scheduling and provide "AI-based personalized task management to online marketplace knowledge sharing" (para. [0074]).” [Applicant’s Remarks, 03/16/2026, page 20]
The Examiner respectfully disagrees. While the Specification refers to AI-based personalization, the claim recites generic machine component and conventional supervised machine learning without specifying any technological improvement in the AI, the computer, or the network. The claim limitations related to assigning tasks, predicting multiple scheduling parameters, and updating calendars reflect automation of human organizational activity, not a solution to a technological problem. Accordingly, this argument is found unpersuasive. The claims should emphasize specific technical improvement to the computer or system, rather than framing the invention solely as automating human task management.
Applicant submits “The claims provide an improvement in the technical field of computerized calendar scheduling and "assisting tools to help people to keep track of tasks" (para. [0003] as filed).” [Applicant’s Remarks, 03/16/2026, page 21]
The Examiner respectfully disagrees. Under Step 2A, Prong Two of the eligibility inquiry, Applicant argues “The claims provide an improvement in the technical field of “assisting tools to help people to keep track of tasks” (para. [0003] as filed).” The additional elements in exemplary claim 1 are: at least one machine including at least one processor, memory, communication platform, a network for online marketplace knowledge sharing, an online sharing platform connecting to a plurality of users, supervised machine learning, training, via supervised machine learning, a plurality of personalized prediction models, based on labels and features extracted from personalized historic data related to past tasks and interactions of the first user and the second user with the past tasks, an application running on a computing device associated with each of the first and second users, the corresponding application, and the application running on each of the computing devices associated with the first and second users, which merely serve to tie the abstract idea to a particular technological environment (computer-based operating environment) via generic computing hardware, software/instructions, which is not sufficient to amount to a practical application, as noted in MPEP 2106.05. Applicant has not provided a persuasive line of reasoning showing how the additional elements are integrated with the abstract idea to integrate the abstract idea into a practical application.
Furthermore, it is noted that Applicant’s claims are devoid of any discernible change, transformation, or improvement to a computer (software or hardware) or any existing technology. Applicant has not shown that any specific technological improvement is achieved within the scope of the claims. It bears emphasis that no machine, processor, memory, communication platform, network, computing device, application, or technological elements are modified or improved upon in any discernible manner. Instead, the result produced by the claims is simply information relating to an assigned session, which is not a technical result or improvement thereof. Nevertheless, even assuming arguendo that an improvement was achieved, improving the process of providing information relating to an assigned session, at most, seems to provide an improvement to a business process using generic computing elements, such that any incidental improvement achieved by automating the claim steps would come from the capabilities of a general-purpose computer rather than the sequence of steps/activities recited in the method itself, which does not materially alter the patent eligibility of the claim. See Bancorp Servs., L.L.C. v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012) (“[T]he fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter.”) (cited in the Federal Circuit's FairWarning decision).
Moreover, the additional elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Accordingly, this argument is found unpersuasive.
Lastly, it is noted that the claimed features related to automated assignment, prediction of multiple scheduling parameters, calendar updates, and project view synchronization are directed to the abstract idea of organizing human tasks and schedules. While these functions may improve the convenience for users, they do not provide a technical improvement to the functioning of the computer itself. For the reasons above, this argument is found unpersuasive.
Applicant submits “the Office Action on pages 21-22 alleges that "[t]he additional elements have been fully considered, but fail to add significantly more because they merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation) by describing the use of generic computing elements to implement the claimed invention, though at a very high level of generality and without imposing meaningful limitation on the scope of the claim, similar to simply saying "apply it" or "apply it using a general purpose computer," which is not enough to transform an abstract idea into eligible subject matter." Applicant respectfully disagrees.” [Applicant’s Remarks, 03/16/2026, page 24]
The Examiner respectfully disagrees. In response, it is noted that the recited features including a processor, memory, network connection, supervised machine learning, calendar access, and project view synchronization do not provide a meaningful limitation beyond implementing the abstract idea on generic computer hardware, The claim merely automates human scheduling and task coordination using conventional computer components. Applying an abstract idea on a generic computer, without improving the computer’s operation itself or introducing a novel technical solution, is not sufficient to confer patent eligibility. For the reasons above, this argument is found unpersuasive.
For the reasons provided above along with the reasons Set forth in the updated §101 rejection below, the amendments and arguments are not sufficient to overcome the §101 rejection.
17. Applicant’s remaining arguments either logically depend from the above-rejected arguments, in which case they too are unpersuasive for the reasons set forth above, or they are directed to features which have been newly added via amendment. Therefore, this is now the Examiner's first opportunity to consider these limitations and as such any arguments regarding these limitations would be inappropriate since they have not yet been examined. A full rejection of these limitations will be presented later in this Office Action.
Claim Rejections - 35 USC § 112
18. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
19. Claims 11-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
20. Claim 11 was amended to recite “Machine readable and non-transitory medium having information recorded thereon for online marketplace knowledge sharing via an online sharing platform leveraging the machine, wherein the information, when read by the machine, causes the online sharing platform to perform the following steps: receiving, to a plurality of users connected to the online sharing platform, from a first user of the plurality of users, information about break-down sessions for different levels of sharing...” Claim 11 recites “machine readable and non-transitory medium having information recorded thereon for online marketplace knowledge sharing via an online sharing platform leveraging the machine.” It is unclear what is meant by the term “leveraging the machine.” Specifically, the claim does not provide sufficient structural or functional definition of how the online sharing platform interacts with, depends on, or utilizes the machines. The term “leveraging” is broad, and it is ambiguous whether the online platform is executed entirely on the machine, a separate component that interacts with the machine, or partially dependent on the machine for certain functions. Examiner suggest clarifying the structural or functional relationship between the online sharing platform and the machine (e.g., by specifying that the online sharing platform is implemented as software executed by the machine; or provide additional specification support to define the meaning of “leveraging the machine” in the context of the claimed invention.
21. All claims dependent from above rejected claims are also rejected due to dependency.
Claim Rejections - 35 USC § 101
22. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
23. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
24. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The eligibility analysis in support of these findings is provided below, in accordance with MPEP 2106.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the claimed method (claims 1-10) and machine readable and non-transitory medium (claims 11-20) are directed to potentially eligible categories of subject matter (i.e., process and article of manufacture, respectively), and therefore claims 1-20 satisfy Step 1 of the eligibility inquiry. With respect to Step 2A Prong One, it is next noted that the claims recite an abstract idea that falls into the “Certain Methods of Organizing Human Activity” abstract idea set forth in MPEP 2106 because the claims recite steps for managing tasks, which encompasses activity for managing personal behavior or relationships or interactions. With respect to independent claim 1, the limitations reciting the abstract idea are indicated in bold below: receiving, by an online sharing platform connecting to a plurality of users, from a first user of the plurality of users, information about break-down sessions for different levels of sharing, wherein a second user of the plurality of users expresses interest in the sharing and a third user does not express interest in the sharing; sending automatically, by the online sharing platform, a recommendation of the break-down sessions to the second user, and a notification of the break-down sessions to the third user; in response to acceptance of at least one of the break-down sessions by the second user, assigning automatically the at least one of the break-down sessions to the second user based on knowledge obtained via supervised machine learning from historic data on previous task completion experience of the second user and previous collaboration between the first user and the second user, wherein the assigned session requires presence of the first and second users; and updating a first calendar associated with the first user and a second calendar associated with the second user by: receiving information related to the assigned session; separately training, via supervised machine learning, a plurality of personalized prediction models, based on different historic training data, respectively; predicting automatically, based on the plurality of separately trained personalized prediction models, multiple scheduling parameters associated with the assigned session, the multiple scheduling parameters including a priority of the assigned session, a duration of the assigned session, and a start time for the assigned session; accessing, via an application running on a computing device associated with each of the first and second users, each of the first and second calendars in a calendar view of the corresponding application; determining, based on the multiple scheduling parameters and existing tasks in the first and second calendars, one or more scheduling constraints for the assigned session; automatically adding the assigned session to the first and second calendars in a manner that satisfies the one or more scheduling constraints to generate updated first and second calendars with the assigned session scheduled therein; and dynamically synchronizing a project view of the application running on each of the computing devices associated with the first and second users with each of the updated first and second calendars to maintain consistency across the project view and the calendar view. These steps cover organizing human activity because the received information directly pertains to user task scheduling. Independent claim 11 recites similar limitations as set forth in claim 1 and are therefore found to recite the same abstract idea as claim 1.
Therefore, because the limitations above set forth activities falling within the “Certain methods of organizing human activity” abstract idea grouping described in MPEP 2106, the additional elements recited in the claims are further evaluated, individually and in combination, under Step 2A Prong Two and Step 2B below.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. The additional elements are: an online sharing platform connecting to a plurality of users, supervised machine learning, separately training, via supervised machine learning, a plurality of personalized prediction models, based on different historic training data, respectively, the plurality of separately trained personalized prediction models, via an application running on a computing device associated with each of the first and second users, the corresponding application, and the application running on each of the computing devices associated with the first and second users (claim 1), machine readable and non-transitory medium having information recorded thereon for online marketplace knowledge sharing via an online sharing platform leveraging the machine, supervised machine learning, separately training, via supervised machine learning, a plurality of personalized prediction models, based on different historic training data, respectively, the plurality of separately trained personalized prediction models, via an application running on a computing device associated with each of the first and second users, the corresponding application, and the application running on each of the computing devices associated with the first and second users with each of the updated first and second calendars to maintain consistency across the project view and the calendar view (claim 11). These additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or computer-executable instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment. See MPEP 2106.05(f) and 2106.05(h). Furthermore, these additional elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.”). Even if the step for receiving is evaluated as an additional element, this activity encompasses, at most, insignificant extra-solution activity, which is not indicative of a practical application, as noted in MPEP 2106.05(g), and is not enough to add significantly more since it is well-understood and conventional activity, as noted in MPEP 2106.05(d)
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are: an online sharing platform connecting to a plurality of users, supervised machine learning, separately training, via supervised machine learning, a plurality of personalized prediction models, based on different historic training data, respectively, the plurality of separately trained personalized prediction models, via an application running on a computing device associated with each of the first and second users, the corresponding application, and the application running on each of the computing devices associated with the first and second users (claim 1), machine readable and non-transitory medium having information recorded thereon for online marketplace knowledge sharing via an online sharing platform leveraging the machine, supervised machine learning, separately training, via supervised machine learning, a plurality of personalized prediction models, based on different historic training data, respectively, the plurality of separately trained personalized prediction models, via an application running on a computing device associated with each of the first and second users, the corresponding application, and the application running on each of the computing devices associated with the first and second users with each of the updated first and second calendars to maintain consistency across the project view and the calendar view (claim 11). The additional elements have been fully considered, but fail to add significantly more because they merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation) by describing the use of generic computing elements to implement the claimed invention, though at a very high level of generality and without imposing meaningful limitation on the scope of the claim, similar to simply saying "apply it” or “apply it using a general purpose computer,” which is not enough to transform an abstract idea into eligible subject matter. Notably, Applicant’s Specification describes generic off-the-shelf computing elements for implementing the claimed invention and suggests that virtually any generic computing devices could be used to implement the invention (See, e.g., Specification paragraphs [0077]: “In this example, the user device on which the present teaching is implemented corresponds to a mobile device 900, including, but is not limited to, a smart phone, a tablet, a music player, a handled gaming console, a global positioning system (GPS) receiver, and a wearable computing device (e.g., an eyeglass such as a smart glass, a wristwatch such as a smart watch, etc.), or in any other form factor. Mobile device 900 may include one or more central processing units (“CPUs”)…”). Therefore, these additional elements describe generic computing elements that merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976. Even if the step for receiving is evaluated as an additional element, this activity encompasses, at most, insignificant extra-solution activity, which is not enough to add significantly more since it is well-understood and conventional activity, as noted in MPEP 2106.05(d).
Even if the supervised machine learning and the plurality of separately trained personalized prediction models were evaluated as elements beyond software/code for a generic computer to execute, it is noted that that the claimed use of supervised machine learning is recited at a high level of generality these elements amount to well-understood, routine, and conventional activity in the art, which fails to add significantly more to the claims. See, e.g., Hughes et al., US 11,210,636 B1 (col. 9, lines 20-22: “perform supervised machine learning using conventional technologies to train each classifier 208.”). See also, Yang et al., US 2021/0406761 A1 (paragraph 0002: “Conventional recommendation models, such as Bayesian Personalized Ranking”).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself.
Dependent claims 2-10 and 12-20 recite the same abstract idea as recited in the independent claims, and when evaluated under Step 2A Prong One of the eligibility inquiry, merely recite further details of the same abstract idea recited in the independent claims accompanied by, at most, the involvement of the same generic computing elements as the independent claims which, as noted above, are not sufficient to amount to a practical application or significantly more than the abstract idea itself. In particular, dependent claims 2-10 recite “wherein the one or more scheduling constraints comprise availability constraints of the first user and the second user, duration constraints derived from the predicted duration, and temporal ordering constraints relative to existing calendar entries in the first and second calendars,” “wherein the different historic training data comprise: first training data related to past task order adjustment by the first and second users, second training data related to past task finish time and length of time to accomplish each past task associated with the first and second users, and third training data related to past task start time and past task finish behaviors of the first and second users,” “wherein the plurality of supervised machine-learned personalized prediction models include: a priority prediction model personalized via training based on the first training data and to be used for predicting a priority of the assigned session in a personalized manner; a duration prediction model personalized via training based on the second training data including statistics about the second user’s past execution of different types of past tasks; and a start time prediction model personalized via training based on the third training data including information indicative of preferences of the second user in start time of different types of past tasks,” “wherein the second calendar associated with the second user has one or more previously scheduled tasks therein; and the updated second calendar includes both the one or more previously scheduled tasks and the assigned session scheduled therein,” “wherein the step of adding the assigned session comprises: obtaining an estimated schedule for the assigned session via: if an entry in the second calendar exists that satisfies the multiple scheduling parameters of the assigned session, estimating the entry as the schedule for the assigned session in the second calendar, and if no entry in the second calendar satisfies the multiple scheduling parameters of the assigned session, estimating a rearrangement of the one or more previously scheduled tasks in the second calendar to create an entry for the assigned session that satisfies the multiple scheduling parameters of the assigned session,” “wherein the entry in the second calendar corresponds to a duration represented by a start time and an end time in the second calendar,” “further comprising performing global optimization with respect to the estimated schedule for the assigned session and the one or more previously scheduled tasks in the second calendar to generate a globally optimized schedule for both the assigned session and the one or more previously scheduled tasks,” “further comprising generating the updated second calendar based on the globally optimized schedule,” “further comprising detecting, prior to the adding the assigned session, any duplicated task by: obtaining a first representation of each of the one or more previously scheduled tasks in the second calendar; obtaining a second representation of the assigned session; determining similarity between the second representation of the assigned task and each of the first representations for the one or more previously scheduled tasks; and removing the assigned session if the similarity satisfies a pre-determined condition,” however these limitations cover organizing human activity since they flow directly from the task scheduling, which encompasses activity for managing personal behavior or relationships or interactions. The other dependent claims have been fully considered as well; however these claims are also directed to the abstract idea itself without integrating it into a practical application and implemented by, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The additional elements recited in the dependent claims include the plurality of supervised machine-learned personalized prediction models (claim 3-4 and 13-14). However, this element is recited at a high level of generality and fails to yield any discernible improvement to the computer or to any technology, nor set forth any additional function or result that provided meaningful limitation beyond linking the abstract idea to a particular technological environment (i.e., automated/computing environment), and thus fail to integrate the abstract idea into a practical application. When evaluated under Step 2A Prong Two and Step 2B, the additional elements do not amount to a practical application or significantly more since they merely require generic computing devices (or computer-implemented instructions/code) which as noted in the discussion of the independent claims above is not enough to render the claims as eligible.
Even if the plurality of supervised machine-learned personalized prediction models were evaluated as elements beyond software/code for a generic computer to execute, as elements beyond software/code for a generic computer to execute, it is noted that that the claimed use of supervised machine learning is recited at a high level of generality these elements amount to well-understood, routine, and conventional activity in the art, which fails to add significantly more to the claims. See, e.g., Hughes et al., US 11,210,636 B1 (col. 9, lines 20-22: “perform supervised machine learning using conventional technologies to train each classifier 208.”).
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself.
For more information, see MPEP 2106.
Allowable over the prior art
25. Claims 1-20 are allowable over prior art. With respect to independent claim 1, the closest prior art, Mitra et al., Dyer, Ganesh et al., and Caligor, collectively teach features for updating a first calendar associated with the first user and a second calendar associated with the second user by: receiving information; accessing, via an application running on a computing device associated with each of the first and second users, each of the first and second calendars in a calendar view of the corresponding application; automatically adding the assigned task to the first and second calendars; and dynamically synchronizing a project view of the application running on each of the computing devices associated with the first and second users with each of the updated first and second calendars [See Office Action mailed 06/12/2025 for prior art citations pertinent to the above-noted subject matter].
With respect to amended independent claim 1, Mitra et al. suggests wherein a second user of the plurality of users expresses interest (paragraph 0110, discussing a proposed new schedule includes several different tasks than the current schedule, and that FIG. 8D also displays an approve option or a decline option to either accept or reject the proposed new schedule), Lhota et al., Pub. No.: US 2019/0295013 A1 suggests assigning automatically to the second user based on knowledge obtained from historic data on previous task completion experience of the second user and previous collaboration between the first user and the second user (paragraph 0022: “The self-learning software code may be executed to determine how many tasks should be assigned to each team member (e.g., a fair share of tasks). The list of team members is initially sorted by associated historical satisfaction scores and the self-learning software code assigns a team member with a lowest historical satisfaction score as a top choice among the tasks to assign. Once a work item is assigned to a team member, the self-learning software code (tool) increments an associated team member's count of assigned tasks and incorporates an estimated satisfaction rating with a historical satisfaction score to create a new projected satisfaction rating.”), and Mullins et al., Pub. No: US 2021/0241231 A1 describes performing automated recommendation or assignment of tasks to users by an automatic task assignment module (paragraph 0058).
However, with respect to amended independent claim 1, the prior art of record does not teach receiving, by an online sharing platform connecting to a plurality of users, from a first user of the plurality of users, information about break-down sessions for different levels of sharing, wherein a second user of the plurality of users expresses interest in the sharing and a third user does not express interest in the sharing; sending automatically, by the online sharing platform, a recommendation of the break-down sessions to the second user, and a notification of the break-down sessions to the third user; in response to acceptance of at least one of the break-down sessions by the second user, assigning automatically the at least one of the break-down sessions to the second user based on knowledge obtained via supervised machine learning from historic data on previous task completion experience of the second user and previous collaboration between the first user and the second user; and separately training, via supervised machine learning, a plurality of personalized prediction models, based on different historic training data, respectively. The prior art of record, including the references previously cited and those additionally reviewed, fails to teach or suggest the claimed combination of features. In particular none of the refences disclose or render obvious the specific arrangement of an online sharing platform configured to receiving from a first user of the plurality of users, information about break-down sessions for different levels of sharing, wherein a second user of the plurality of users expresses interest in the sharing and a third user does not express interest in the sharing; sending automatically, by the online sharing platform, a recommendation of the break-down sessions to the second user, and a notification of the break-down sessions to the third user, and separately training, via supervised machine learning, a plurality of personalized prediction models, based on different historic training data, respectively.
The following is a statement of reasons for the indication of allowable subject matter: The claims are directed to allowable subject matter because the prior art of record either individually or in combination does not teach: “A method implemented on at least one machine including at least one processor, memory, and communication platform capable of connecting to a network for online marketplace knowledge sharing, comprising: receiving, by an online sharing platform connecting to a plurality of users, from a first user of the plurality of users, information about break-down sessions for different levels of sharing, wherein a second user of the plurality of users expresses interest in the sharing and a third user does not express interest in the sharing; sending automatically, by the online sharing platform, a recommendation of the break-down sessions to the second user, and a notification of the break-down sessions to the third user; in response to acceptance of at least one of the break-down sessions by the second user, assigning automatically the at least one of the break-down sessions to the second user based on knowledge obtained via supervised machine learning from historic data on previous task completion experience of the second user and previous collaboration between the first user and the second user, wherein the assigned session requires presence of the first and second users; and updating a first calendar associated with the first user and a second calendar associated with the second user by: receiving information related to the assigned session; separately training, via supervised machine learning, a plurality of personalized prediction models, based on different historic training data, respectively; predicting automatically based on the plurality of separately trained personalized prediction models multiple scheduling parameters associated with the assigned session, the multiple scheduling parameters including a priority of the assigned session, a duration of the assigned session, and a start time for the assigned session; accessing, via an application running on a computing device associated with each of the first and second users, each of the first and second calendars in a calendar view of the corresponding application; determining, based on the multiple scheduling parameters and existing tasks in the first and second calendars, one or more scheduling constraints for the assigned session; automatically adding the assigned session to the first and second calendars in a manner that satisfies the one or more scheduling constraints to generate updated first and second calendars with the assigned session scheduled therein; and dynamically synchronizing a project view of the application running on each of the computing devices associated with the first and second users with each of the updated first and second calendars to maintain consistency across the project view and the calendar view,” as recited in amended claim 1 (and as similarly encompassed by claim 11), thus rendering claims 1-20 as allowable over prior art. However, these claims are not allowable because claims 1-20 they remain rejected 35 U.S.C. 101 and claims 11-20 remain rejected under 35 U.S.C. 112(b).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Frisco et al., Pub. No.: US 2003/0061330 A1 – describes a web-based collaborative project and process management solution.
Cranshaw, Justin, et al. "Calendar. help: Designing a workflow-based scheduling agent with humans in the loop." Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. 2017 – describes a system that provides fast, efficient scheduling through structured workflows.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Darlene Garcia-Guerra/
Primary Examiner, Art Unit 3625